Progress has been made in developing and using both heart rate and accerometer based motion sensors to predict physical activity (PA) level. However, both of these methods have inherent weaknesses when used in isolation, and do not provide sufficient accuracy to satisfy the research need. For this reason, advancement has been made by integrating these two objective assessment methodologies into single unit devices. To date, single unit devices remain plagued by weaknesses, pertaining to the lack of simplified modeling strategies to utilize the data, the need for complicated laboratory calibrations, and devices being cumbersome to wear. The aims of this proposal address these weaknesses in integrative heart rate and accelerometer- based PA assessment methodologies. The specific aims are: 1) To develop and validate conditional models to integrate physiological and movement data to improves estimates of physical activity intensity (PAI); 2) To evaluate different dynamic field calibration activities to individualize calibration standards to estimate PAI for use in conditional modeling approaches; 3) To compare the integrative sensor approach to single heart rate estimates (FLEX heart rate approach) and single accelerometer estimates (regression cut-point and regression equation approach) for assessing PA and physical activity related energy expenditure (PAEE) during different short duration simulated everyday lifestyle activities conducted in a laboratory and field setting; and 4) To compare the integrative sensor approach to single heart rate estimates (FLEX heart rate approach) and single accelerometer estimates (regression cut-point and regression equation approach) for assessing PAEE and total daily energy expenditure (TDEE) during an extended period of free-living. Our qualified research team will address the above aims by first carrying out individual dynamic laboratory calibrations using the integrative sensor approach, and developing individualized heart rate and accelerometer calibrations relative to indirect calorimetry. Based upon these results the laboratory data will be modeled to explore conditional integrative models to predict PAI levels. Field dynamic calibrations will then be conducted for integration into the conditional models to explore the use of field calibrations rather than the need for laboratory calibration to individualize heart rate data. The resulting model and most precise field calibration standard will then be utilized to predict PAEE from collected heart rate and accelerometer data and compared to indirect calorimetry PAEE measures during simulated lifestyle activities. This integrative approach will be further validated during a period of free-living activity, with PAEE and TDEE estimates compared to the doubly labeled water technique. The resulting conditional modeling approach to predict PAEE will be implemented into popular commercial software packages and made available to activity researchers. The results of the proposed series of studies will move the field of PA assessment forward by providing innovative approaches to obtain valid and reliable estimates of PA from integrative heart rate and accelerometer data. This will enable researchers to employ this technique to determine behavioral change due to an activity intervention, and further our understanding of the dose-response relationship between PA and health.